With the continuously escalating cost of health care, one significant issue is balancing the cost of health care versus the quality of health care. While some percentage of health care dollars are wasted on dubious or inappropriate care, it is also true that, even in the United States, there is still preventable disease and death that occurs. The question is how to assure the highest possible quality of care without incurring excessive costs.
One area of health care where the cost-quality debate rages is in the care of pregnant women, where the intent is to deliver healthy babies while preserving the health of the mother. Current national health objectives have targeted reductions in the following areas: infant deaths (before, during, and after birth); low birth weight; severe complications of pregnancy; and instances of severe mental retardation. Current obstetrical practices include: identifying risk factors; monitoring fetal heart rate and maternal uterine contractions (with a Doppler ultrasound device and a tocodynamometer, respectively); ultrasound measurements; amniocentesis; and other laboratory tests which were virtually unknown twenty years ago. Unfortunately, the use of these various monitoring and measurement methods have not significantly improved infant health. Currently-available methods for categorizing pregnancies as healthy or potentially at risk do not appear to be sufficiently accurate and reliable. In addition, since these various monitoring and measurement methods have been implemented, it is believed that the cesarean delivery rate has dramatically increased. While the cesarean delivery rate is now on a slight decline, national health objectives also include a targeted reduction in the cesarean delivery rate.
Much of antepartum care (occurring in physicians' offices from conception until labor) and all of intrapartum care (occurring in the hospital while the patient is in labor) is concerned with the early identification of potentially adverse situations and subsequent early intervention to avoid or minimize adverse events. Yet, there is no highly sensitive and specific technology to assess fetal well-being. A method that accurately and reliably categorizes pregnancies as healthy or potentially at risk is needed to improve perinatal outcomes while managing health care costs.
Typical intrapartum monitoring includes electronic fetal monitoring (EFM) which is often made up of a dual-track strip chart or display of both fetal heart rate (FHR) and uterine contractions (UC). Obstetricians currently determine when it is advisable to intervene (delivering the child by cesarean section as opposed to vaginally) by viewing the EFM data and relying upon their past training and experience. Because of the great uncertainty inherent in this approach, it is natural for obstetricians to seek to err on the side of a cesarean section, which is more likely to result in a safe delivery for a fetus, even though the cesarean section may not have been necessary; is much more costly; and has a potentially greater health impact on the mother. Thus, a much larger number of cesarean sections end up being performed than are necessary. For all of these reasons, a reliable predictor of fetal outcomes would be very useful in assisting obstetricians in determining when it is appropriate to intervene in the delivery process.
Others have proposed systems for automatically assessing fetal health based upon the fetal heart rate. An example of this is disclosed by Dawes, et al., Int. J. Biomed. Comput., 25 (1990) pp. 287-294, "Criteria for the Design of Fetal Heart Rate Analysis Systems," and Dawes, et al., Obstetrics & Gynecology, Vol. 80, No. 4, October 1992, pp. 673-678, "Short-term Fetal Heart Rate Variation, Decelerations, and Umbilical Flow Velocity Waveforms Before Labor." The Dawes articles suggest measuring the short-term time variations in the fetal heart rate and determining that the fetal health is in jeopardy when the variations fall below a predetermined threshold. It is believed that such approaches are not sufficiently robust. Furthermore they do not reflect and predict fetal outcomes with sufficient accuracy. It is believed that this lack of robustness is due in part to a lack of any type of artificial intelligence that would allow a system to adapt to and learn about new situations and an inability to accurately assess fetal health from noisy data.
It is against this background, and the desire to solve the problems of and improve on the prior art, that the above invention has been developed.